System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis
Background: The System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs). Objective: The aim of this stu...
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Published in | JMIR mHealth and uHealth Vol. 10; no. 8; p. e37290 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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JMIR Publications
18.08.2022
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Abstract | Background: The System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs). Objective: The aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs. Methods: A search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution. Results: The mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (–0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for “physical activity” apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding “physical activity” apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98). Conclusions: This study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs. |
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AbstractList | Background: The System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs). Objective: The aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs. Methods: A search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution. Results: The mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (–0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for “physical activity” apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding “physical activity” apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98). Conclusions: This study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs. The System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs).BACKGROUNDThe System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs).The aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs.OBJECTIVEThe aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs.A search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution.METHODSA search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution.The mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (-0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for "physical activity" apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding "physical activity" apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98).RESULTSThe mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (-0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for "physical activity" apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding "physical activity" apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98).This study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs.CONCLUSIONSThis study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs. BackgroundThe System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs). ObjectiveThe aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs. MethodsA search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution. ResultsThe mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (–0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for “physical activity” apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding “physical activity” apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98). ConclusionsThis study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs. |
Author | Dix, Alan Bond, Raymond Mulvenna, Maurice Bai, Lu Leigh, Simon Hunt, Sophie Hyzy, Maciej |
AuthorAffiliation | 2 Organisation for the Review of Care and Health Applications Daresbury United Kingdom 4 Institute of Digital Healthcare University of Warwick Coventry United Kingdom 3 Computational Foundry Swansea University Swansea United Kingdom 1 School of Computing Ulster University Newtownabbey United Kingdom |
AuthorAffiliation_xml | – name: 2 Organisation for the Review of Care and Health Applications Daresbury United Kingdom – name: 3 Computational Foundry Swansea University Swansea United Kingdom – name: 1 School of Computing Ulster University Newtownabbey United Kingdom – name: 4 Institute of Digital Healthcare University of Warwick Coventry United Kingdom |
Author_xml | – sequence: 1 givenname: Maciej orcidid: 0000-0003-0791-8976 surname: Hyzy fullname: Hyzy, Maciej – sequence: 2 givenname: Raymond orcidid: 0000-0002-1078-2232 surname: Bond fullname: Bond, Raymond – sequence: 3 givenname: Maurice orcidid: 0000-0002-1554-0785 surname: Mulvenna fullname: Mulvenna, Maurice – sequence: 4 givenname: Lu orcidid: 0000-0003-1242-5412 surname: Bai fullname: Bai, Lu – sequence: 5 givenname: Alan orcidid: 0000-0002-5242-7693 surname: Dix fullname: Dix, Alan – sequence: 6 givenname: Simon orcidid: 0000-0002-6843-6447 surname: Leigh fullname: Leigh, Simon – sequence: 7 givenname: Sophie orcidid: 0000-0001-9515-6582 surname: Hunt fullname: Hunt, Sophie |
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ContentType | Journal Article |
Copyright | 2022. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Maciej Hyzy, Raymond Bond, Maurice Mulvenna, Lu Bai, Alan Dix, Simon Leigh, Sophie Hunt. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.08.2022. Maciej Hyzy, Raymond Bond, Maurice Mulvenna, Lu Bai, Alan Dix, Simon Leigh, Sophie Hunt. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.08.2022. 2022 |
Copyright_xml | – notice: 2022. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Maciej Hyzy, Raymond Bond, Maurice Mulvenna, Lu Bai, Alan Dix, Simon Leigh, Sophie Hunt. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.08.2022. – notice: Maciej Hyzy, Raymond Bond, Maurice Mulvenna, Lu Bai, Alan Dix, Simon Leigh, Sophie Hunt. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.08.2022. 2022 |
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References_xml | – ident: ref1 – ident: ref37 doi: 10.2196/26779 – year: 2016 ident: ref8 publication-title: Quantifying the User Experience: Practical Statistics for User Research. 2nd ed – ident: ref7 – ident: ref16 doi: 10.2196/mhealth.9232 – year: 2020 ident: ref34 publication-title: Jakob's Law – ident: ref22 doi: 10.2196/17481 – ident: ref15 doi: 10.2196/mhealth.9069 – ident: ref24 doi: 10.23919/cisti.2019.8760678 – ident: ref27 doi: 10.6004/jadpro.2015.6.5.8 – ident: ref38 doi: 10.2196/11500 – ident: ref6 doi: 10.1177/2327857919081018 – ident: ref20 doi: 10.1177/1541931213601496 – ident: ref13 doi: 10.1109/icacsis.2016.7872776 – ident: ref35 doi: 10.7309/jmtm.5.1.6 – ident: ref39 doi: 10.2196/19099 – ident: ref19 doi: 10.2196/26873 – ident: ref18 doi: 10.2196/mhealth.9651 – ident: ref14 doi: 10.1080/10447318.2020.1801173 – ident: ref29 doi: 10.2196/24909 – ident: ref21 doi: 10.1145/3051488.3051492 – ident: ref26 doi: 10.3390/s150613159 – ident: ref4 – ident: ref25 doi: 10.2196/jmir.2951 – ident: ref2 – ident: ref36 doi: 10.1177/0890117118790394 – ident: ref5 doi: 10.1186/s12911-020-1033-3 – ident: ref32 doi: 10.1007/978-3-030-17705-8_11 – ident: ref17 doi: 10.1155/2016/1604609 – ident: ref23 doi: 10.2196/16925 – ident: ref40 doi: 10.2196/12160 – ident: ref11 doi: 10.1037/0033-2909.86.3.638 – ident: ref30 doi: 10.2196/mental.9445 – ident: ref3 doi: 10.1136/bmjopen-2021-053891 – ident: ref31 doi: 10.1145/3429360.3468207 – ident: ref12 doi: 10.1097/00007632-200012150-00014 – volume: 199 start-page: 35 year: 2014 ident: ref28 publication-title: Stud Health Technol Inform – ident: ref10 doi: 10.1016/j.ijmedinf.2019.03.018 – volume: 8 start-page: 29 issue: 2 year: 2013 ident: ref9 publication-title: J Usability Stud – ident: ref33 |
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Title | System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis |
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