Leveraging a Consumer-Based Product to Develop a Cancer-Specific Mobile Meditation App: Prototype Development Study
Notice bibliographique
Résumé
BACKGROUND: Mobile meditation apps may offer a long-term, accessible, and effective solution for ongoing symptom management in cancer patients/survivors. However, there are currently no commercial cancer-specific meditation apps that reflect cancer specialist expertise, input from cancer patients/survivors, and features and content specific to cancer patients'/survivors' needs. OBJECTIVE: The aim of this study was to gain insight (via surveys, daily journals, and focus groups) from cancer patients/survivors, health care providers, and current subscribers of Calm (a consumer-based mobile meditation app) who were patients/survivors to develop a prototype of a mobile meditation app specifically designed for cancer patients/survivors. METHODS: Participants were recruited via prior partnerships, word-of-mouth referrals, and recruitment posts on Facebook and Instagram. Cancer patients/survivors and health care providers were instructed to download and use the Calm app for at least 10 minutes a day for 7 days, complete an online daily journal for 7 days, and participate in a virtual focus group (one for cancer patients/survivors and one for providers). Current Calm subscribers who were cancer patients/survivors completed an online survey about different aspects of the Calm app and participated in a third virtual focus group. Data were qualitatively analyzed using a combination of deductive and inductive coding. RESULTS: A total of 27 participants (11 cancer patients/survivors, 10 health care providers, 6 current Calm subscribers) completed the study. Similar themes and subthemes were found across surveys, daily journals, and focus groups, and fell into two major categories, content and functionality, with cancer-specific and noncancer-specific themes identified within each category. The majority of content preferences and suggestions that arose were cancer-specific, such as content related to negative emotions or feelings (eg, anxiety, grief, trauma/posttraumatic stress disorder, fear of recurrence, isolation), positive feelings and finding meaning (eg, gratitude, storytelling, acceptance), scenarios and experiences (eg, waiting, treatment-specific mediations), type and stage of cancer journey, and movement modifications. Some of the noncancer-specific themes under app content included sleep, music, and visualizations. In terms of app functionality, the majority of participants expressed interest in having a section/tab/area of the app that was specifically geared toward cancer patients/survivors. Preferences and suggestions for cancer-specific functionality features included options based on symptoms or journey, being able to communicate with other patients or survivors to share suggestions for specific meditations, and having an emergency toolkit for patients/survivors. CONCLUSIONS: Findings from cancer patients/survivors, health care providers, and current Calm subscribers who were patients/survivors to be incorporated into the development of the prototype fell into two major categories: (1) content of the app and (2) functionality of the app. The prototype's form and function will be pilot-tested among 30 cancer patients/survivors in a 4-week study, and the resulting feasibility data will be used to inform the final app design and an efficacy study.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 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 ».