Usability and Satisfaction Testing of Game-Based Learning Avatar-Navigated Mobile (GLAm), an App for Cervical Cancer Screening: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Barriers to cervical cancer screening in young adults include a lack of knowledge and negative perceptions of testing. Evidence shows that mobile technology reduces these barriers; thus, we developed a web app, Game-based Learning Avatar-navigated mobile (GLAm), to educate and motivate cervical cancer screening using the Fogg Behavioral Model as a theoretic guide. Users create avatars to navigate the app, answer short quizzes with education about cervical cancer and screening, watch videos of the screening process, and earn digital trophies. OBJECTIVE: We tested ease of use, usefulness, and satisfaction with the GLAm app among young adults. METHODS: This mixed methods study comprised a qualitative think-aloud play interview session and a quantitative survey study. Participants were cervical cancer screening-eligible US residents aged 21 to 29 years recruited through social media. Qualitative study participants explored the app in a think-aloud play session conducted through videoconference. Data were analyzed using directed content analysis to identify themes of ease of use, usefulness, and content satisfaction. Qualitative study participants and additional participants then used the app independently for 1 week and completed a web-based survey (the quantitative study). Ease of use, usefulness, and satisfaction were assessed using the validated Technology Acceptance Model and Computer System Usability Questionnaire adapted to use of an app. Mean (SD) scores (range 1-7) are presented. RESULTS: A total of 23 individuals participated in one or both study components. The mean age was 25.6 years. A majority were cisgender women (21/23, 91%) and White (18/23, 78%), and 83% (19/23) had at least some secondary education. Nine participants completed the think-aloud play session. Direct content analysis showed desire for content that is concise, eases anxiety around screenings, and uses game features (avatars and rewards). Twenty-three individuals completed the quantitative survey study. Mean scores showed the app was perceived to be easy to use (mean score 6.17, SD 0.27) and moderately useful to increase cervical cancer screening knowledge and uptake (mean score 4.94, SD 0.27). Participants were highly satisfied with the app (mean score 6.21, SD 1.20). CONCLUSIONS: Survey results showed participants were satisfied with the app format and found it easy to use. The app was perceived to be moderately useful to inform and motivate cervical cancer screening; notably, the screening reminder function was not tested in this study. Qualitative study results demonstrated the app's ability to ease anxiety about screening through demonstration of the screening process, and brevity of app components was favored. Interpretation of results is limited by the predominantly cisgender, White, and educated study population; additional testing in populations which historically have lower cervical cancer screening uptake is needed. A modified version of the app is undergoing efficacy testing in a randomized clinical trial.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».