Designing a Smartphone-Based Virtual Reality App for Relaxation: Qualitative Crossover Study
Notice bibliographique
Résumé
BACKGROUND: Accumulating evidence supports the use of virtual reality (VR) in mental health care, with one potential application being its use to assist individuals with relaxation exercises. Despite studies finding support for the potential of VR to effectively aid in relaxation, its implementation remains limited outside of specialized clinics. Known barriers are insufficient knowledge regarding VR operation, lack of availability of VR relaxation apps tailored to local health care systems, and cost concerns. Unfortunately, many VR relaxation apps are designed exclusively for stand-alone headsets, limiting accessibility for a broad audience. OBJECTIVE: We aimed to design an accessible, smartphone-based VR relaxation app based on user preferences. This paper describes the assessment of 2 stand-alone VR relaxation apps and the resulting smartphone-based VR relaxation app design. METHODS: Overall, 30 participants (n=23, 77% women; n=7, 23% men) took part in 2 separate VR sessions, assessing 1 of the 2 VR relaxation apps (Flowborne and Calm Place) in each session. After each session, participants were presented with open-ended questions to assess their experiences via a web-based survey tool. These questions explored positive and negative features, shortcomings, and suggestions for improvements while also allowing space for additional remarks concerning the 2 VR relaxation apps. Three of the authors analyzed the responses using inductive thematic analysis, a process comprising 6 phases. RESULTS: Across both the apps, 5 recurring themes and 13 recurring subthemes were identified in the participants' answers: audio (music and sounds, guidance), visuals (content, realism, variation and dynamics in the environment), features (language, options, feedback and instructions, duration, exercise), implementation (technical aspects, cybersickness, acceptability and usability), and experience. We analyzed the participants' findings and conducted a literature review, which served as the basis for developing the app. The resulting app is a Dutch-language, smartphone-based VR relaxation app, with customization options including 3 types of relaxation exercises, 2 guiding voices, and 3 different environments. Efforts have been made to ensure maximum variation and dynamism in the environments. Calming music and nature sounds accompany the exercises. The efficacy and effectiveness of the resulting app design were not assessed. CONCLUSIONS: This study provides insights into key features of VR relaxation apps, which were subsequently used for the development of a novel smartphone-based VR relaxation app. Further research concerning the effectiveness of this app, along with a broader evaluation of the efficacy and user feedback for smartphone-based VR relaxation apps, is needed. More generally, there is a clear need for more research on the impact of interactivity, biofeedback, and type of environment in VR relaxation.
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 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 | 0,017 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».