Integrating Key User Characteristics in User-Centered Design of Digital Support Systems for Seniors’ Physical Activity Interventions to Prevent Falls: Protocol for a Usability Study
Notice bibliographique
Résumé
BACKGROUND: The goal of user-centered design (UCD) is to understand the users' perspective and to use that knowledge to shape more effective solutions. The UCD approach provides insight into users' needs and requirements and thereby improves the design of the developed services. However, involving users in the development process does not guarantee that feedback from different subgroups of users will shape the development in ways that will make the solutions more useful for the entire target user population. OBJECTIVE: The aim of this study was to describe a protocol for systematic analysis and prioritization of feedback from user subgroups in the usability testing of a digital motivation support for fall-preventive physical activity (PA) interventions in seniors (aged 65 years and older). This protocol can help researchers and developers to systematically exploit feedback from relevant user subgroups in UCD. METHODS: ) possible user subgroups. The presented method enables systematic tracking of the user subgroups' contributions in iterative development. The method comprises (1) compilation of difficulties and deficiencies in the digital applications identified in usability testing, (2) clustering of the identified difficulties and deficiencies, and (3) prioritization of deficiencies to be rectified. Tracking user subgroup representation in the user feedback ensures that the development process is prioritized according to the needs of different subgroups. Mainly qualitative data collection methods are used. RESULTS: A protocol was developed to ensure that feedback from users representing all possible variants of 3 selected key user characteristics (gender, PA level, and level of technology experience) is considered in the iterative usability testing of a digital support for seniors' PA. The method was applied in iterative usability testing of two digital applications during spring/summer 2018. Results from the study on the users' experiences and the iterative modification of the digital applications are expected to be published during 2021. CONCLUSIONS: Methods for systematic collection, analysis, and prioritization of feedback from user subgroups might be particularly important in heterogenous user groups (eg, seniors). This study can contribute to identifying and improving the understanding of potential differences between user subgroups of seniors in their use and experiences of digital support for fall-preventive PA interventions. This knowledge may be relevant for developing digital support systems that are appropriate, useful, and attractive to users and for enabling the design of digital support systems that target specific user subgroups (ie, tailoring of the support). The protocol needs to be further used and investigated in order to validate its potential value. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/20061.
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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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».