Developing Usability Heuristics for Grid Computing Applications: Lessons Learned

Quiñones, Daniela; Rusu, Cristián; Roncagliolo, Silvana; Rusu, Virginica; Collázos, César A.

doi:10.1007/978-3-319-32467-8_43

Cited by 2 publications

(8 citation statements)

References 16 publications

(17 reference statements)

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“…In this stage, the characteristics defined in step 3 are specified as a heuristic, following a structured format: ID, name, definition, explanation of how the heuristic was developed, example(s) of when a system being evaluated complies with or violates the heuristic, expected benefits if the system complies with the heuristic, and anticipated problems of heuristic misunderstanding [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, steps 5 and 6 are expected to refine the example, expected benefits, and anticipated problems of each heuristic. Therefore, the heuristics presented here may still be revised later on [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of these, only one methodology known as the R3C [ 42 ] has been used extensively (14 times) [ 41 , 43 , 44 ]. The authors have since improved it [ 45 ]. Another widespread approach is to point out the limitations of existing heuristics, in particular [ 23 ], and build on these by exploring other aspects that need to be considered for the target domain.…”

Section: Methodsmentioning

confidence: 99%

“…The development of a set of heuristics for PGHD enablement was primarily guided by the R3C methodology most utilized and applied in the literature [ 45 ]. However, a closer inspection of the methodology reveals that neither its development [ 44 ] nor its application to a case study [ 47 ] were described in explicit detail, leaving room for flexibility and interpretation for future implementations.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the development of a specialized set of heuristics for K-SRS technologies was guided by the R3C [ 45 ]. Where relevant, each step was grounded through the best practice activities in the literature, that is, analysis of the context of use through a literature review, interviews, and focus groups, and by using the original heuristics [ 23 ] as a basis for determining the new set of heuristics.…”

Section: Methodsmentioning

confidence: 99%

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Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics

2020

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Background An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients’ use of PGHD and lower the risk of negative effects. Objective This study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study. Methods The development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen’s heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics. Results A total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement. Conclusions This study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics

2020

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Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics (Preprint)

Dimaguila¹,

Gray²,

Merolli³

2019

Preprint

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BACKGROUND An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialised contexts, and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in healthcare; and where a number of remote patient monitoring technologies do not yet enable patient use of their PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients’ use of PGHD, and lower the risk of negative effects. OBJECTIVE In this paper, we describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We use the case of Kinect-based stroke rehabilitation systems (K-SRS). METHODS The development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development, nor its application to a case study were described in explicit detail. Thus where relevant, each step was grounded through best practice activities in the literature; and using Nielsen’s heuristics as a basis for determining the new set of heuristics. As such, this paper builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics. RESULTS Eight new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described above. It is hoped that this would aid future developers of specialised heuristics, who could apply the detailed process of heuristic development for other domains of technology; and additionally for the case of PGHD enablement, for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and is intended to result in a robust and credible set of heuristics, prior to validation and refinement. CONCLUSIONS This paper is the first to develop a new set of specialised heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. Additionally, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialised set of heuristics.

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Developing Usability Heuristics for Grid Computing Applications: Lessons Learned

Cited by 2 publications

References 16 publications

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics (Preprint)

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