ArticleApplied clinical informatics2010
Improving Clinical Trial Participant Tracking Tools Using Knowledge-anchored Design Methodologies.
Article in Applied clinical informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Usage Metrics of Web-Based Interventions Evaluated in Randomized Controlled Trials: Systematic Review.Journal of medical Internet research · 2020Pooled it
- Validating the information technology (IT) implementation framework to Implement mHealth technology for consumers: A case study of the Sense2Quit app for smoking cessation.International journal of medical informatics · 2025Article
- Perspectives on the design and methodology of periconceptional nutrient supplementation trials.Trials · 2016Article
- Advancing user experience research to facilitate and enable patient-centered research: current state and future directions.EGEMS (Washington, DC) · 2013Article
- The TOKEn project: knowledge synthesis for in silico science.Journal of the American Medical Informatics Association : JAMIA · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveRigorous human-computer interaction (HCI) design methodologies have not traditionally been applied to the development of clinical trial participant tracking (CTPT) tools. Given the frequent us of iconic HCI models in CTPTs, and prior evidence of usability problems associated with the use of ambiguous icons in complex interfaces, such approaches may be problematic. Presentation Discovery (PD), a knowledge-anchored HCI design method, has been previously demonstrated to improve the design of iconic HCI models. In this study, we compare the usability of a CTPT HCI model designed using PD and an intuitively designed CTPT HCI model.
methodsAn iconic CPTP HCI model was created using PD. The PD-generated and an existing iconic CTPT HCI model were subjected to usability testing, with an emphasis on task accuracy and completion times. Study participants also completed a qualitative survey instrument to evaluate subjective satisfaction with the two models.
resultsCTPT end-users reliably and reproducibly agreed on the visual manifestation and semantics of prototype graphics generated using PD. The performance of the PD-generated iconic HCI model was equivalent to an existing HCI model for tasks at multiple levels of complexity, and in some cases superior. This difference was particularly notable when tasks required an understanding of the semantic meanings of multiple icons.
conclusionThe use of PD to design an iconic CTPT HCI model generated beneficial results and improved end-user subjective satisfaction, while reducing task completion time. Such results are desirable in information and time intensive domains, such as clinical trials management.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.