Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objectives: Mobile health applications that are designed without considering usability criteria can lead to cognitive overload,resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluatedfor cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitiveoverload for the user. Methods: In this study, we evaluated a mobile personal health records application using the cognitivetask analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the relatedupdated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasksand categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updatedGOMS model and gesture-level model. Results: All 10 identified tasks were split into 398 steps consisting of mental andphysical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMSmodel and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks accordingto the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded anaverage of 0.80, indicating good reliability for the evaluation method. Conclusions: The majority of the task execution timescomprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation,the number of mental operators should be reduced.
Abstract
Objectives: Mobile health applications that are designed without considering usability criteria can lead to cognitive overload,resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluatedfor cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitiveoverload for the user. Methods: In this study, we evaluated a mobile personal health records application using the cognitivetask analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the relatedupdated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasksand categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updatedGOMS model and gesture-level model. Results: All 10 identified tasks were split into 398 steps consisting of mental andphysical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMSmodel and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks accordingto the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded anaverage of 0.80, indicating good reliability for the evaluation method. Conclusions: The majority of the task execution timescomprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation,the number of mental operators should be reduced.
참고문헌 (28)
- Tang PC, "The missing link : bridging the patient-provider health information gap", Health Aff(Millwood), 24(5), pp.1290~1295 (2005)[DOI]
- Showell C, "Barriers to the use of personal health records by patients : a structured review", PeerJ, 5, pp.e3268~ (2017)[DOI]
- "Digital 2021 October Global Statshot Report" (2022)[URL]
- "11 surprising mobile health statistics"[URL]
- Pachunka E, "Natural-setting PHR usability evaluation using the NASA TLX to measure cognitive load of patients", Proceedings of the 52nd Hawaii International Conference on System Sciences, pp.3954~3963 (2016)
- Ehrler F, "A Mobile app(BEDSide Mobility)to support nurses’ tasks at the patient’s bedside : usability study", JMIR Mhealth Uhealth, 6(3), pp.e57~ (2018)[DOI]
- International Organization for Standardization, "Ergonomics of human-system interaction - Part 11: Usability:Definitions and concepts", International Organization for Standardization, ~ (2018)
- Baddeley A, "Working memory : theories, models, and controversies", Annu Rev Psychol, 63, pp.1~29 (2012)[DOI]
- Cooper G, "Research into cognitive load theory and instructional design at UNSW", pp.1998
- "Voice user interface design: minimizing cognitive load" (2004)[URL]
- "Use of eyetracking technology to investigate cognitive load theory" (2018)[URL]
- Tracy JP, "Measuring cognitive load to test the usability of web sites", Proceedings of the 53rd Annual Conference of the Society for Technical Communication, pp.256~260 (2006)
- Militello LG, "Applied cognitive task analysis(ACTA) : a practitioner’s toolkit for understanding cognitive task demands", Ergonomics, 41(11), pp.1618~1641 (1998)[DOI]
- Saitwal H, "Assessing performance of an Electronic Health Record(EHR)using Cognitive Task Analysis", Int J Med Inform, 79(7), pp.501~506 (2010)[DOI]
- Kushniruk AW, "Evaluating the organizational impact of healthcare information systems", pp.144~173 (2005), Springer
- Klein G, "Advances in human performance and cognitive engineering research", pp.163~199 (2001), Emerald Group Publishing Limited
- Card SK, "The psychology of humancomputer interaction" (2018), CRC Press
- Rasmussen R, "The long and twisting path : an efficiency evaluation of an electronic whiteboard system", Stud Health Technol Inform, 183, pp.174~178 (2013)
- Gross M, "Improving medical device usability by reducing complexity using a novel predictive models-based user interface assessment tool", Hum Factors Healthc, 3, pp.100041~ (2023)[DOI]
- Setthawong P, "Updated Goals Operators Methods and Selection Rules (GOMS) with touch screen operations for quantitative analysis of user interfaces", Int J Adv Sci Eng Inf Technol, 9(1), pp.258~265 (2019)[DOI]
- Card SK, "The keystroke-level model for user performance time with interactive systems", Commun ACM, 23(7), pp.396~410 (1980)[DOI]
- Nystrom A, "Gesture-level model : a modified Keystrokelevel model for tasks on mobile touchscreen devices", master, pp.2018
- 김다미, "A Unit Touch Gesture Model of Performance Time Prediction for Mobile Devices", 대한인간공학회지, 35(4), pp.277~291 (2016), 대한인간공학회[DOI]
- Li H, "Extended KLM for mobile phone interaction : a user study result", Proceedings of the Extended Abstracts on Human Factors in Computing Systems, pp.3517~3522 (2010)[DOI]
- "General directorate of health information systems" (2021)[URL]
- Bonnie E. John, "Using GOMS for user interface design and evaluation", ACM Transactions on Computer-Human Interaction, 3(4), pp.287~319 (1996), Association for Computing Machinery (ACM)[DOI]
- Al Ghalayini M, "Too much or too little? Investigating the usability of high and low data displays of the same electronic medical record", Health Informatics J, 26(1), pp.88~103 (2020)[DOI]
- Suebnukarn S, "Usability assessment of an electronic health record in a comprehensive dental clinic", SpringerPlus, 2, pp.220~ (2013)[DOI]