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      Analysis of the concurrent validity and reliability of five common clinical goniometric devices

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          Abstract

          Measurement errors play an important role in the development of goniometric equipment, devices used to measure range of motion. Reasonable validity and reliability are critical for both the device and examiner before and after to testing in human subjects. The objective is to evaluate the concurrent validity and reliability of five different clinical goniometric devices for the purpose of establishing an acceptable measurement error margin for a novel device. We explored the validity and inter- and intrarater reliability scores of five goniometric devices namely (i) the universal goniometer (UG), a two-armed hand-held goniometer, (ii) the inclinometer (IC), featuring a single base, fluid level, and gravity-weighted inclinometer, (iii) the digital inclinometer (DI), functioning as both a DI and dynamometer, (iv) the smartphone application (SA), employing gyroscope-based technology within a smartphone platform application and (v) the modified inclinometer (MI), a gravity pendulum-based inclinometer equipped with a specialized fixing apparatus. Measurements were obtained at 12 standard angles and 8 human shoulder flexion angles ranging from 0° to 180°. Over two testing sessions, 120 standardized angle measurements and 160 shoulder angle measurements from 20 shoulders were repetitively taken by three examiners for each device. The intraclass correlation coefficient (ICC), standard error of measurement (SEM), and minimal detectable change (MDC) were calculated to assess reliability and validity. Concurrent validity was also evaluated through the execution of the 95% limit of agreement (95% LOA) and Bland–Altman plots, with comparisons made to the UG. The concurrent validity for all device pairs was excellent in both study phases (ICC > 0.99, 95% LOA − 4.11° to 4.04° for standard angles, and − 10.98° to 11.36° for human joint angles). Inter- and intrarater reliability scores for standard angles were excellent across all devices (ICC > 0.98, SEM 0.59°–1.75°, MDC 1°–4°), with DI showing superior reliability. For human joint angles, device reliability ranged from moderate to excellent (ICC 0.697–0.975, SEM 1.93°–4.64°, MDC 5°–11° for inter-rater reliability; ICC 0.660–0.996, SEM 0.77°–4.06°, MDC 2°–9° for intra-rater reliability), with SA demonstrating superior reliability. Wider angle measurement however resulted in reduced device reliability. In conclusion, our study demonstrates that it is essential to assess measurement errors independently for standard and human joint angles. The DI is the preferred reference for standard angle testing, while the SA is recommended for human joint angle testing. Separate evaluations across the complete 0°–180° range offer valuable insights.

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          Most cited references44

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
            • Record: found
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            • Article: not found

            Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM.

            Reliability, the consistency of a test or measurement, is frequently quantified in the movement sciences literature. A common metric is the intraclass correlation coefficient (ICC). In addition, the SEM, which can be calculated from the ICC, is also frequently reported in reliability studies. However, there are several versions of the ICC, and confusion exists in the movement sciences regarding which ICC to use. Further, the utility of the SEM is not fully appreciated. In this review, the basics of classic reliability theory are addressed in the context of choosing and interpreting an ICC. The primary distinction between ICC equations is argued to be one concerning the inclusion (equations 2,1 and 2,k) or exclusion (equations 3,1 and 3,k) of systematic error in the denominator of the ICC equation. Inferential tests of mean differences, which are performed in the process of deriving the necessary variance components for the calculation of ICC values, are useful to determine if systematic error is present. If so, the measurement schedule should be modified (removing trials where learning and/or fatigue effects are present) to remove systematic error, and ICC equations that only consider random error may be safely used. The use of ICC values is discussed in the context of estimating the effects of measurement error on sample size, statistical power, and correlation attenuation. Finally, calculation and application of the SEM are discussed. It is shown how the SEM and its variants can be used to construct confidence intervals for individual scores and to determine the minimal difference needed to be exhibited for one to be confident that a true change in performance of an individual has occurred.
              • Record: found
              • Abstract: found
              • Article: not found

              Understanding Bland Altman analysis

              In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods.
In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement.
The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot.
The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals.
The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.

                Author and article information

                Contributors
                siriratk@go.buu.ac.th
                bhornluck@go.buu.ac.th
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 November 2023
                27 November 2023
                2023
                : 13
                : 20931
                Affiliations
                [1 ]Department of Physical Therapy, Faculty of Allied Health Sciences, Burapha University, ( https://ror.org/01ff74m36) Chonburi, 20131 Thailand
                [2 ]Faculty of Engineering, Mahidol University, ( https://ror.org/01znkr924) Phutthamonthon, Nakhon Pathom, Thailand
                [3 ]Burapha University International College, Burapha University, ( https://ror.org/01ff74m36) Chonburi, 20131 Thailand
                [4 ]Corrosion Lab, Multi-Physical Engineering Sciences Group (MPESG), Department of Mechanical/Aeronautical Engineering, Salford University, ( https://ror.org/01tmqtf75) 3-08, SEE Building, Manchester, M54WT UK
                Article
                48344
                10.1038/s41598-023-48344-6
                10684565
                38017058
                028421f5-a099-4966-a8e2-3f0a0564791b
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 February 2023
                : 25 November 2023
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                © Springer Nature Limited 2023

                Uncategorized
                physical examination,biomedical engineering
                Uncategorized
                physical examination, biomedical engineering

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