Geospatial assistive technologies for wheelchair users: a scoping review of usability measures and criteria for mobile user interfaces and their potential applicability
Notice bibliographique
Résumé
Background: Wheelchair users are increasingly using route planners and navigation systems to help them get around the city. The absence of a list of usability criteria for wheelchair user-centred design and recommending geospatial assistive technologies creates uncertainty about the choices to be made by rehabilitation clinicians and geographic information systems specialists. The aim of this study was to compile such a list by identifying usability criteria from standardized questionnaires linked to user interfaces and geospatial assistive technologies (GATs).Material and methods: We conducted a scoping review in ACM Digital Library, Inspec/Compendex and PsycINFO for the period 2005–2016 using keyword strategies. From 84 articles identified, after screening and exclusion procedures, 15 articles were selected. Data were extracted from them and reported in table 1 (relevant questionnaires listed in alphabetical order, type of user interface, population studied, psychometric properties, type of measurement scale and information about the construct, number of subscales and items) and in table 2 (usability criteria up to 20 items for the questionnaires, scales or constructs, pointing criteria as gold standard in physical rehabilitation and as in geographic information).Results: We identified 87 usability criteria in 12 standardized questionnaires in 15 articles (with at least two types of psychometric properties). There are 54 usability criteria that could be used in clinical situations concerning their potential applicability to GATs for wheelchair users: 20 are familiar to rehabilitation clinicians who recommend assistive technologies, 21 are generic to GATs while 13 are specific to mobile applications or voice recognition systems. It remains 34 criteria that are not actually familiar to clinicians: actual use, content (including content-clarity, content-color, content-consistency, content-credibility, content-legibility, content-relevance, content-trustworthy, and content-understandable), control-obviousness, customer service behavior, delivery format, design-application, ease of navigation, entry-point type, everyday words, fingertip-size controls, font, functions-expected, functions-integration, gestalt, graphics, habit, hierarchy, input, network externality, speech characteristics, structure, subtle animation, time spent waiting, transition, user goal orientation and verbosity.Conclusions: More research is needed to develop a questionnaire specific to geospatial assistive technologies for wheelchair users linked with mobile applications and information content.Implications for rehabilitationFor manual wheelchair users paired with geospatial assistance technology, “effectiveness, efficiency, learnability and satisfaction” are essential criteria for route planning and navigation task.Clinicians can optimize the selection of a geospatial assistance technology considering 16 criteria: appearance, assistance-human support, comfort, ease of holding, ease of use, emotional aspect, endurance, facilitating conditions, intention to use, minimal memory load, physical effort, price value, simplicity, social influence, training and usefulness.Clinicians should have in mind that WC users want to plan a route with as few obstacles as possible. Information on the screen should be accessible to WC users (text, contrast, symbols, graphics, photos, voice, vibration, route views). Hands are occupied with the hand rims, WC users would prefer “listen to verbal” instructions to continue their route instead of looking on their electronic device. 34 criteria are specific for route planning and navigation task.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,009 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».