Development of a Smartphone App for Informal Carers of People With Cancer: Processes and Learnings
Notice bibliographique
Résumé
BACKGROUND: There are few support systems available to informal carers who provide care to cancer patients. Smartphone apps have the capacity to reach large audiences and can provide information and support at a time convenient to carers. OBJECTIVE: The aim of this study was to design a smartphone app prototype for carers of adults with cancer. METHODS: A multiple-method design was used to develop a smartphone app. Current and past carers of people with any type of cancer were recruited from a public hospital, a private hospital, and a carer organization, who participated in either a focus group or phone interview. Carers answered questions about items to include in an app to address supportive care needs identified. Using carers' feedback, a smartphone app was designed and tested. Beta testing was conducted using a convenience sample of participants who completed scenarios to inform the app's design, functionality, and usability. Scenarios were timed and marked as complete or incomplete. Participants completed a questionnaire about the usability of the app. Beta testing occurred in 2 stages-a paper-based version of the app and an app-based test using the participants' preferred device. Alpha testing was completed internally to ensure the functionality of the app. Data were collected between May 2016 and August 2017. RESULTS: A total of 33 carers participated in phone interviews and 12 in focus groups; their average age was 55 (SD 14) years, and 60% (27/45) were female. The majority of carers (76%, 25/33) had a positive attitude toward using smartphone apps. Carers noted that smartphone technology might improve their ability to seek information and support in managing their own health as well as the care needs of the person with cancer. Carers requested a variety of information and resources to be included in the app. Paper-based testing included the following: participants (N=10) were aged above 30 years (30%, 3/10), 30 to 49 years (30%, 3/10), and 50 years or above (40%, 4/10), and 60% (6/10) were male. Participants found the app user-friendly and pleasing in appearance. App-based testing included the following: participants (N=10) were aged above 30 years (20%, 2/10), 30 to 49 years (30%, 3/10), and 50 years or above (50%, 5/10), and 50% (5/10) were male. Participants reported the app to be user-friendly and easy to navigate. The majority (60%, 6/10) of participants were unable to create a shortcut icon to add the app to the home screen of their phone. CONCLUSIONS: Carers highlighted the needed information and support to assist them during the caring period; they also reported having a positive attitude toward smartphone apps. The Carer Guide App is currently undergoing a pilot study to further test usability among carers of people with 1 cancer type.
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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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,001 |
| 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 ».