The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach
Open accessAbstract
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 evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user.Methods: In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model.Results: All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method.Conclusions: The majority of the task execution times comprised 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.
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Cites
Working Memory: Theories, Models, and Controversies
Baddeley, A. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100422 · Cited by 4,271
The keystroke-level model for user performance time with interactive systems
Card, S. K., Moran, T. P., & Newell, A. (1980) · Communications of the ACM · doi:10.1145/358886.358895 · Cited by 1,174
Militello, L. G., & Hutton, R. J. B. (1998) · Ergonomics · doi:10.1080/001401398186108 · Cited by 607
Using GOMS for user interface design and evaluation
John, B. E., & Kieras, D. E. (1996) · ACM Transactions on Computer-Human Interaction · doi:10.1145/235833.236050 · Cited by 397
The Missing Link: Bridging The Patient–Provider Health Information Gap
Tang, P. C., & Lansky, D. J. (2005) · Health Affairs · doi:10.1377/hlthaff.24.5.1290 · Cited by 242
Assessing performance of an Electronic Health Record (EHR) using Cognitive Task Analysis
Saitwal, H., Feng, X., Walji, M. F., Patel, V. L., & Zhang, J. (2010) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2010.04.001 · Cited by 98
Barriers to the use of personal health records by patients: a structured review
Showell, C. (2017) · PeerJ · doi:10.7717/peerj.3268 · Cited by 72
4. Some guidelines for conducting a cognitive task analysis
Klein, G. L., & Militello, L. G. (2004) · Advances in human performance and cognitive engineering research · doi:10.1016/s1479-3601(01)01006-2 · Cited by 69
A Mobile App (BEDSide Mobility) to Support Nurses’ Tasks at the Patient's Bedside: Usability Study
Ehrler, F., Weinhold, T., Joe, J., Lovis, C., & Blondon, K. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9079 · Cited by 39
Extended KLM for mobile phone interaction
Li, H., Liu, Y., Liu, J., Wang, X., Li, Y., & Rau, P. P. (2010) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1753846.1754011 · Cited by 22
Ghalayini, M. A., Antoun, J., & Moacdieh, N. M. (2018) · Health Informatics Journal · doi:10.1177/1460458218813725 · Cited by 20
Cognitive Approaches to the Evaluation of Healthcare Information Systems
Kushniruk, A. W., & Patel, V. L. (2006) · Springer eBooks · doi:10.1007/0-387-30329-4_6 · Cited by 10
The Long and Twisting Path: An Efficiency Evaluation of an Electronic Whiteboard System
Rasmussen, R. O., & Kushniruk, A. W. (2013) · Studies in health technology and informatics · doi:10.3233/978-1-61499-203-5-174 · Cited by 8
Setthawong, P., & Setthawong, R. (2019) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.9.1.7865 · Cited by 8
Natural-Setting PHR Usability Evaluation using the NASA TLX to Measure Cognitive Load of Patients
Pachunka, E., Windle, J. R., Schuetzler, R. M., & Fruhling, A. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.480 · Cited by 8
Usability assessment of an electronic health record in a comprehensive dental clinic
Suebnukarn, S., Rittipakorn, P., Thongyoi, B., Boonpitak, K., Wongsapai, M., & Pakdeesan, P. (2013) · SpringerPlus · doi:10.1186/2193-1801-2-220 · Cited by 4
Gross, M., Roberts, C., Stinson, K., & Wiltman, S. A. (2023) · Human Factors in Healthcare · doi:10.1016/j.hfh.2023.100041 · Cited by 4
A Unit Touch Gesture Model of Performance Time Prediction for Mobile Devices
Kim, D., & Myung, R. (2016) · Journal of the Ergonomics Society of Korea · doi:10.5143/jesk.2016.35.4.277 · Cited by 3
Research works printed but not linked: 1
- Tracy, Measuring cognitive load to test the usability of web sites, 256
Cited by
Sorted by citations
Shen, Y., Wang, S., Shen, Y., Tan, S., Dong, Y., Qin, W., & Zhuang, Y. (2024) · Healthcare · doi:10.3390/healthcare12131310 · Cited by 23
A fuzzy Kano model proposal for sustainable product design: Mobile application feature analysis
Karakurt, N. F., & Çebi, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112824 · Cited by 18
Wen, Z., Yang, J., Sun, B., & Liu, Y. (2025) · Symmetry · doi:10.3390/sym17040502 · Cited by 14
Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
Hadad, Y., Bensimon, M., Ben‐Shimol, Y., & Greenberg, S. (2024) · Applied Sciences · doi:10.3390/app14198911 · Cited by 7
Erjavec, K., & Kotnik, P. (2024) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.2478/pielxxiw-2024-0026 · Cited by 3
Metadata
- Authors
- Zayim, N., Yıldız, H., & Yüce, Y. K.
- Affiliations
- Akdeniz University (TR)
- Corresponding author
- Yıldız, Hasibe
- Venue
- Healthcare Informatics Research
- Published
- 2023-10-31
- Volume
- 29
- Issue
- 4
- Pages
- 367-376
- ISSN
- 2093-3681 (ISSN-L)
- 2093-369X
- PMC
- PMC10651402
- OpenAlex
- W4388478682
- Cited by
- 16
- References
- 18
- Citations by year
- 2026: 10 · 2025: 3 · 2024: 3
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare Informatics Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Alanya Alaaddin Keykubat University Institutional Repository (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Akdeniz University Scientific Research Project Administration Division (5416)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- cognitive load, mobile health apps, personal health records, GOMS model, usability evaluation, user interface design, task completion time, inter-rater reliability
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