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Record W4361990302 · doi:10.2196/41359

Methodologies for Evaluating the Usability of Rehabilitation Technologies Aimed at Supporting Shared Decision-Making: Scoping Review

2023· article· en· W4361990302 on OpenAlexafffundvenue
Rehab Alhasani, Nicole George, Dennis Radman, Claudine Auger, Sara Ahmed

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsCentre de réadaptation Lethbridge-Layton-MackayMcGill UniversityMcGill University Health CentreUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationPrincess Nourah Bint Abdulrahman University
KeywordsUsabilityCINAHLWeb usabilityUsability engineeringComputer sciencePsycINFOUsability goalsCognitive walkthroughHeuristic evaluationKnowledge managementMEDLINEHuman–computer interactionPsychological interventionMedicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The field of rehabilitation has seen a recent rise in technologies to support shared decision-making (SDM). Usability testing during the design process of SDM technologies is needed to optimize adoption and realize potential benefits. There is variability in how usability is defined and measured. Given the complexity of usability, a thorough examination of the methodologies used to measure usability to develop the SDM technologies used in rehabilitation care is needed. OBJECTIVE: This scoping review aims to answer the following research questions: which methods and measures have been used to produce knowledge about the usability of rehabilitation technologies aimed at supporting SDM at the different phases of development and implementation? Which parameters of usability have been measured and reported? METHODS: This review followed the Arksey and O'Malley framework. An electronic search was performed in the Ovid MEDLINE, Embase, CINAHL, and PsycINFO databases from January 2005 up to November 2020. In total, 2 independent reviewers screened all retrieved titles, abstracts, and full texts according to the inclusion criteria and extracted the data. The International Organization for Standardization framework was used to define the scope of usability (effectiveness, efficiency, and satisfaction). The characteristics of the studies were outlined in a descriptive summary. Findings were categorized based on usability parameters, technology interventions, and measures of usability. RESULTS: A total of 38 articles were included. The most common SDM technologies were web-based aids (15/33, 46%). The usability of SDM technologies was assessed during development, preimplementation, or implementation, using 14 different methods. The most frequent methods were questionnaires (24/38, 63%) and semistructured interviews (16/38, 42%). Satisfaction (27/38, 71%) was the most common usability parameter mapped to types of SDM technologies and usability evaluation methods. User-centered design (9/15, 60%) was the most frequently used technology design framework. CONCLUSIONS: The results from this scoping review highlight the importance and the complexity of usability evaluation. Although various methods and measures were shown to be used to evaluate the usability of technologies to support SDM in rehabilitation, very few evaluations used in the included studies were found to adequately span the selected usability domains. This review identified gaps in usability evaluation, as most studies (24/38, 63%) relied solely on questionnaires rather than multiple methods, and most questionnaires simply focused on the usability parameter of satisfaction. The consideration of end users (such as patients and clinicians) is of particular importance for the development of technologies to support SDM, as the process of SDM itself aims to improve patient-centered care and integrate both patient and clinician voices into their rehabilitation care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.160
metaresearch head score (Gemma)0.331
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.160
Threshold uncertainty score0.844

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1600.331
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0110.010
Bibliometrics0.0880.061
Science and technology studies0.0050.005
Scholarly communication0.0140.012
Open science0.0060.007
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.359
GPT teacher head0.575
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations9
Published2023
Admission routes3
Has abstractyes

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